


'''simple docstring'''


import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_						:  List[Any]				  =						get_tests_dir('fixtures/spiece.model')





@require_sentencepiece
@require_tokenizers
class        lowercase__    (		_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: int       =    AlbertTokenizer
							A_				: Any       =    AlbertTokenizerFast
							A_				: List[str]       =    True
							A_				: int       =    True
							A_				: int       =    True






							def UpperCAmelCase_      (       self    ):
											super().setUp()

											# We have a SentencePiece fixture for testing
											_SCREAMING_SNAKE_CASE					:			Any							= AlbertTokenizer(__snake_case    )
											tokenizer.save_pretrained(self.tmpdirname    )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """this is a test"""
											_SCREAMING_SNAKE_CASE					:			Tuple							= """this is a test"""
											return input_text, output_text






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= """<pad>"""
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= 0

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case    )       ,   __snake_case    )
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case    )       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= list(self.get_tokenizer().get_vocab().keys()    )

											self.assertEqual(vocab_keys[0]       ,   """<pad>"""    )
											self.assertEqual(vocab_keys[1]       ,   """<unk>"""    )
											self.assertEqual(vocab_keys[-1]       ,   """▁eloquent"""    )
											self.assertEqual(len(__snake_case    )       ,   3_0000    )






							def UpperCAmelCase_      (       self    ):
											self.assertEqual(self.get_tokenizer().vocab_size       ,   3_0000    )






							def UpperCAmelCase_      (       self    ):
											if not self.test_rust_tokenizer:
															return

											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_tokenizer()
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_rust_tokenizer()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= """I was born in 92000, and this is falsé."""

											_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer.tokenize(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= rust_tokenizer.tokenize(__snake_case    )
											self.assertListEqual(__snake_case       ,   __snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= tokenizer.encode(__snake_case       ,   add_special_tokens=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= rust_tokenizer.encode(__snake_case       ,   add_special_tokens=__snake_case    )
											self.assertListEqual(__snake_case       ,   __snake_case    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_rust_tokenizer()
											_SCREAMING_SNAKE_CASE					:			List[Any]							= tokenizer.encode(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= rust_tokenizer.encode(__snake_case    )
											self.assertListEqual(__snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= AlbertTokenizer(__snake_case       ,   keep_accents=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= tokenizer.tokenize("""This is a test"""    )
											self.assertListEqual(__snake_case       ,   ["""▁this""", """▁is""", """▁a""", """▁test"""]    )

											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )       ,   [48, 25, 21, 1289]    )

											_SCREAMING_SNAKE_CASE					:			int							= tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
											self.assertListEqual(
											    __snake_case       ,   ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]    )
											_SCREAMING_SNAKE_CASE					:			Any							= tokenizer.convert_tokens_to_ids(__snake_case    )
											self.assertListEqual(__snake_case       ,   [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

											_SCREAMING_SNAKE_CASE					:			int							= tokenizer.convert_ids_to_tokens(__snake_case    )
											self.assertListEqual(
											    __snake_case       ,   ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]       ,   )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= AlbertTokenizer(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer.encode("""sequence builders"""    )
											_SCREAMING_SNAKE_CASE					:			int							= tokenizer.encode("""multi-sequence build"""    )

											_SCREAMING_SNAKE_CASE					:			int							= tokenizer.build_inputs_with_special_tokens(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= tokenizer.build_inputs_with_special_tokens(__snake_case       ,   __snake_case    )

											assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
											assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
											    tokenizer.sep_token_id
											]






							@slow
							def UpperCAmelCase_      (       self    ):
											# fmt: off
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= {"""attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """input_ids""": [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=__snake_case       ,   model_name="""albert-base-v2"""       ,   revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"""       ,   )








'''simple docstring'''


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase_						:  Optional[Any]				  =						16
UpperCAmelCase_						:  List[str]				  =						32







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ = 16				, SCREAMING_SNAKE_CASE__ = "bert-base-cased"	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= load_dataset("""glue"""				, """mrpc"""	)

				def tokenize_function(SCREAMING_SNAKE_CASE__	):
								# max_length=None => use the model max length (it's actually the default)
								_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer(examples["""sentence1"""]				, examples["""sentence2"""]				, truncation=SCREAMING_SNAKE_CASE__				, max_length=SCREAMING_SNAKE_CASE__	)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				_SCREAMING_SNAKE_CASE					:			str							= datasets.map(
				    SCREAMING_SNAKE_CASE__				, batched=SCREAMING_SNAKE_CASE__				, remove_columns=["""idx""", """sentence1""", """sentence2"""]				, load_from_cache_file=SCREAMING_SNAKE_CASE__	)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				_SCREAMING_SNAKE_CASE					:			Tuple							= tokenized_datasets.rename_column("""label"""				, """labels"""	)

				def collate_fn(SCREAMING_SNAKE_CASE__	):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(SCREAMING_SNAKE_CASE__				, padding="""max_length"""				, max_length=128				, return_tensors="""pt"""	)
								return tokenizer.pad(SCREAMING_SNAKE_CASE__				, padding="""longest"""				, return_tensors="""pt"""	)

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE					:			int							= DataLoader(
				    tokenized_datasets["""train"""]				, shuffle=SCREAMING_SNAKE_CASE__				, collate_fn=SCREAMING_SNAKE_CASE__				, batch_size=SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= DataLoader(
				    tokenized_datasets["""validation"""]				, shuffle=SCREAMING_SNAKE_CASE__				, collate_fn=SCREAMING_SNAKE_CASE__				, batch_size=SCREAMING_SNAKE_CASE__	)

				return train_dataloader, eval_dataloader







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				model.eval()
				_SCREAMING_SNAKE_CASE					:			Dict							= 0
				for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								with torch.no_grad():
												_SCREAMING_SNAKE_CASE					:			Tuple							= model(**SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= outputs.logits.argmax(dim=-1	)
								# It is slightly faster to call this once, than multiple times
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= accelerator.gather(
								    (predictions, batch["""labels"""])	)  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(SCREAMING_SNAKE_CASE__	) - 1:
																_SCREAMING_SNAKE_CASE					:			List[str]							= predictions[: len(eval_dataloader.dataset	) - samples_seen]
																_SCREAMING_SNAKE_CASE					:			List[Any]							= references[: len(eval_dataloader.dataset	) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=SCREAMING_SNAKE_CASE__				, references=SCREAMING_SNAKE_CASE__				, )

				_SCREAMING_SNAKE_CASE					:			str							= metric.compute()
				return eval_metric["accuracy"]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= config["""lr"""]
				_SCREAMING_SNAKE_CASE					:			Any							= int(config["""num_epochs"""]	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= int(config["""seed"""]	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= int(config["""batch_size"""]	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= args.model_name_or_path

				set_seed(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= get_dataloaders(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__				, return_dict=SCREAMING_SNAKE_CASE__	)

				# Instantiate optimizer
				_SCREAMING_SNAKE_CASE					:			List[str]							= (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				_SCREAMING_SNAKE_CASE					:			Any							= optimizer_cls(params=model.parameters()				, lr=SCREAMING_SNAKE_CASE__	)

				if accelerator.state.deepspeed_plugin is not None:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 1
				_SCREAMING_SNAKE_CASE					:			int							= (len(SCREAMING_SNAKE_CASE__	) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								_SCREAMING_SNAKE_CASE					:			List[Any]							= get_linear_schedule_with_warmup(
								    optimizer=SCREAMING_SNAKE_CASE__				, num_warmup_steps=0				, num_training_steps=SCREAMING_SNAKE_CASE__				, )
				else:
								_SCREAMING_SNAKE_CASE					:			Any							= DummyScheduler(SCREAMING_SNAKE_CASE__				, total_num_steps=SCREAMING_SNAKE_CASE__				, warmup_num_steps=0	)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Tuple							= accelerator.prepare(
				    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# We need to keep track of how many total steps we have iterated over
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 0
				# We also need to keep track of the stating epoch so files are named properly
				_SCREAMING_SNAKE_CASE					:			str							= 0
				_SCREAMING_SNAKE_CASE					:			Tuple							= evaluate.load("""glue"""				, """mrpc"""	)
				_SCREAMING_SNAKE_CASE					:			int							= num_epochs

				if args.partial_train_epoch is not None:
								_SCREAMING_SNAKE_CASE					:			Dict							= args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint	)
								_SCREAMING_SNAKE_CASE					:			Any							= args.resume_from_checkpoint.split("""epoch_"""	)[1]
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """"""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								_SCREAMING_SNAKE_CASE					:			int							= int(SCREAMING_SNAKE_CASE__	) + 1
								_SCREAMING_SNAKE_CASE					:			List[str]							= evaluation_loop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								accelerator.print("""resumed checkpoint performance:"""				, SCREAMING_SNAKE_CASE__	)
								accelerator.print("""resumed checkpoint's scheduler's lr:"""				, lr_scheduler.get_lr()[0]	)
								accelerator.print("""resumed optimizers's lr:"""				, optimizer.param_groups[0]["""lr"""]	)
								with open(os.path.join(args.output_dir				, f"""state_{starting_epoch-1}.json"""	)				, """r"""	) as f:
												_SCREAMING_SNAKE_CASE					:			Any							= json.load(SCREAMING_SNAKE_CASE__	)
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

    # Now we train the model
				_SCREAMING_SNAKE_CASE					:			int							= {}
				for epoch in range(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):
								model.train()
								for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= model(**SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= outputs.loss
												_SCREAMING_SNAKE_CASE					:			int							= loss / gradient_accumulation_steps
												accelerator.backward(SCREAMING_SNAKE_CASE__	)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								_SCREAMING_SNAKE_CASE					:			int							= f"""epoch_{epoch}"""
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= os.path.join(args.output_dir				, SCREAMING_SNAKE_CASE__	)
								accelerator.save_state(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= evaluation_loop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Dict							= accuracy
								_SCREAMING_SNAKE_CASE					:			Any							= lr_scheduler.get_lr()[0]
								_SCREAMING_SNAKE_CASE					:			Any							= optimizer.param_groups[0]["""lr"""]
								_SCREAMING_SNAKE_CASE					:			Dict							= epoch
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= overall_step
								accelerator.print(f"""epoch {epoch}:"""				, SCREAMING_SNAKE_CASE__	)

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir				, f"""state_{epoch}.json"""	)				, """w"""	) as f:
																json.dump(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
				parser.add_argument(
				    """--model_name_or_path"""				, type=SCREAMING_SNAKE_CASE__				, default="""bert-base-cased"""				, help="""Path to pretrained model or model identifier from huggingface.co/models."""				, required=SCREAMING_SNAKE_CASE__				, )
				parser.add_argument(
				    """--output_dir"""				, type=SCREAMING_SNAKE_CASE__				, default="""."""				, help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				, )
				parser.add_argument(
				    """--resume_from_checkpoint"""				, type=SCREAMING_SNAKE_CASE__				, default=SCREAMING_SNAKE_CASE__				, help="""If the training should continue from a checkpoint folder."""				, )
				parser.add_argument(
				    """--partial_train_epoch"""				, type=SCREAMING_SNAKE_CASE__				, default=SCREAMING_SNAKE_CASE__				, help="""If passed, the training will stop after this number of epochs."""				, )
				parser.add_argument(
				    """--num_epochs"""				, type=SCREAMING_SNAKE_CASE__				, default=2				, help="""Number of train epochs."""				, )
				_SCREAMING_SNAKE_CASE					:			str							= parser.parse_args()
				_SCREAMING_SNAKE_CASE					:			int							= {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

				training_function(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				main()




